7 Amazing Examples Of Computer And Machine Vision In Practice

7 Amazing Examples
And Machine Vision
In Practice
Of Computer

© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
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Text
IntroductionIntroduction
Even though early experiments in computer vision started in the 1950s and it was first put to
use commercially to distinguish between typed and handwritten text by the 1970s, today the
applications for computer vision have grown exponentially. By 2022, the computer vision
and hardware market is expected to reach $48.6 billion.
It is such a part of everyday life you likely experience computer vision regularly even if you
don't always recognize when and where the technology is deployed. Here is what computer
vision is, how it works and seven amazing examples in practice today.
7 Amazing Examples Of Computer And Machine
Vision In Practice

What is Computer Vision (CV)?
Computer vision is a form of artificial intelligence where computers can “see” the world,
analyze visual data and then make decisions from it or gain understanding about the
environment and situation. One of the driving factors behind the growth of computer vision is
the amount of data we generate today that is then used to train and make computer vision
better. Our world has countless images and videos from the built-in cameras of our mobile
devices alone. But while images can include photos and videos, it can also mean data from
thermal or infrared sensors and other sources.
Along with a tremendous amount of visual data (more than 3 billion images are shared online
every day), the computing power required to analyze the data is now accessible and more
affordable. As the field of computer vision has grown with new hardware and algorithms so
has the accuracy rates for object identification. In less than a decade, today’s systems have
reached 99 percent accuracy from 50 percent making them more accurate than humans at
quickly reacting to visual inputs.

How Does Computer Vision Work?
One of the critical components to realizing all the capabilities of artificial intelligence is to give
machines the power of vision. To emulate human sight, machines need to acquire, process
and analyze and understand images. The tremendous growth in achieving this milestone was
made thanks to the iterative learning process made possible with neural networks.
It starts with a curated dataset with information that helps the machine learn a specific topic.
If the goal is to identify videos of cats as it was for Google in 2012, the dataset used by the
neural networks needs to have images and videos with cats as well as examples without cats.
Each image needs to be tagged with metadata that indicates the correct answer. When a
neural network runs through data and signals it's found an image with a cat; it's the feedback
that is received regarding if it was correct or not that helps it improve. Neural networks are
using pattern recognition to distinguish many different pieces of an image. Instead of a
programmer defining the attributes that make a cat such as having a tail and whiskers, the
machines learn from the millions of images uploaded.

7 Amazing Examples of Computer Vision
Imagine all the things human sight allows and you can start to realize the nearly
endless applications for computer vision. Here are some of the most exciting
examples of computer vision in practice today:
Autonomous Vehicles
Computer vision is necessary to enable self-driving cars.
Manufacturers such as Tesla, BMW, Volvo, and Audi use
multiple cameras, lidar, radar, and ultrasonic sensors to acquire
images from the environment so that their self-driving cars can
detect objects, lane markings, signs and traffic signals to safely
drive.

Google Translate app
All you need to do to read signs in a foreign language is to
point your phone’s camera at the words and let the Google
Translate app tell you what it means in your preferred language
almost instantly. By using optical character recognition to see
the image and augmented reality to overlay an accurate
translation, this is a convenient tool that uses computer vision.

Facial Recognition
China is definitely on the cutting edge of using facial
recognition technology, and they use it for police work,
payment portals, security checkpoints at the airport and even to
dispense toilet paper and prevent theft of the paper at Tiantan
Park in Beijing, among many other applications.

Healthcare
Since 90 percent of all medical data is image based there is a
plethora of uses for computer vision in medicine. From enabling
new medical diagnostic methods to analyze X-rays,
mammography and other scans to monitoring patients to
identify problems earlier and assist with surgery, expect that our
medical institutions and professionals and patients will benefit
from computer vision today and even more in the future as its
rolled out in healthcare.

Real-time Sports Tracking
Ball and puck tracking on televised sports has been common for
a while now, but computer vision is also helping play and
strategy analysis, player performance and ratings, as well as to
track the brand sponsorship visibility in sports broadcasts.

Agriculture
At CES 2019, John Deere featured a semi-autonomous combine
harvester that uses artificial intelligence and computer vision to
analyze grain quality as it gets harvested and to find the
optimal route through the crops. There’s also great potential for
computer vision to identify weeds so that herbicides can be
sprayed directly on them instead of on the crops. This is
expected to reduce the amount of herbicides needed by 90
percent.

Manufacturing
Computer vision is helping manufacturers run more safely,
intelligently and effectively in a variety of ways. Predictive
maintenance is just one example where equipment is monitored
with computer vision to intervene before a breakdown would
cause expensive downtime. Packaging and product quality are
monitored, and defective products are also reduced with
computer vision.

There is already a tremendous amount of real-world applications for computer
vision, and the technology is still young. As humans and machines continue to
partner, the human workforce will be freed up to focus on higher-value tasks
because the machines will automate processes that rely on image recognition.

© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website
© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website

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7 Amazing Examples Of Computer And Machine Vision In Practice

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7 Amazing Examples Of Computer And Machine Vision In Practice